Resting-state　functional　magnetic　resonance　imaging　(RFMRI)　enables　researchers　to　monitor　fluctuations　in　the　spontaneous　brain　activities　of　thousands　of　regions　in　the　human　brain　simultaneously,　representing　a　popular　tool　for　macro-scale　functional　connectomics　to　characterize　normal　brain　function,　mind-brain　associations,　and　the　various　disorders.　However,　the　test-retest　reliability　of　RFMRI　remains　largely　unknown.　We　review　previously　published　papers　on　the　test-retest　reliability　of　voxel-wise　metrics　and　conduct　a　meta-summary　reliability　analysis　of　seven　common　brain　networks.　This　analysis　revealed　that　the　heteromodal　associative　(default,　control,　and　attention)　networks　were　mostly　reliable　across　the　seven　networks.　Regarding　examined　metrics,　independent　component　analysis　with　dual　regression,　local　functional　homogeneity　and　functional　homotopic　connectivity　were　the　three　mostly　reliable　RFMRI　metrics.　These　observations　can　guide　the　use　of　reliable　metrics　and　further　improvement　of　test-retest　reliability　for　other　metics　in　functional　connectomics.　We　discuss　the　main　issues　with　low　reliability　related　to　sub-optimal　design　and　the　choice　of　data　processing　options.　Future　research　should　use　large-sample　test-retest　data　to　rectify　both　the　within-subject　and　between-subject　variability　of　RFMRI　measurements　and　accelerate　the　application　of　functional　connectomics.　(C)　2014　Elsevier　Ltd.　All　rights　reserved.